Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Tiancheng Xia, Richard Jiang, Yong Qing Fu, Nanlin Jin

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)
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Abstract

Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices.
Original languageEnglish
Article number012048
JournalIOP Conference Series: Materials Science and Engineering
Volume646
Issue numberconference 1
DOIs
Publication statusPublished - 17 Oct 2019
Event2019 3rd International Conference on Artificial Intelligence Applications and Technologies (AIAAT 2019) - Beijing, China
Duration: 1 Aug 20193 Aug 2019

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